Text Classification
Transformers
Safetensors
English
multilingual
xlm-roberta
multi-label-classification
multi-head-classification
disaster-response
humanitarian-aid
social-media
twitter
Generated from Trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use spencercdz/xlm-roberta-sentiment-requests with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use spencercdz/xlm-roberta-sentiment-requests with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="spencercdz/xlm-roberta-sentiment-requests")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("spencercdz/xlm-roberta-sentiment-requests") model = AutoModel.from_pretrained("spencercdz/xlm-roberta-sentiment-requests", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 144
Browse files- model.safetensors +1 -1
- training_log.csv +1 -0
model.safetensors
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training_log.csv
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141.0,0.3223490325319251,0.711591375256391,0.15152114629745483,14.5283,177.103,5.575,0.24485036921881073,92778
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| 143 |
142.0,0.32296927865154607,0.711128927193466,0.15144237875938416,14.5853,176.41,5.554,0.24757092887679752,93436
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| 144 |
143.0,0.32139610646170486,0.7116607420017023,0.15139997005462646,14.4324,178.279,5.612,0.24523902059852312,94094
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| 142 |
141.0,0.3223490325319251,0.711591375256391,0.15152114629745483,14.5283,177.103,5.575,0.24485036921881073,92778
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| 143 |
142.0,0.32296927865154607,0.711128927193466,0.15144237875938416,14.5853,176.41,5.554,0.24757092887679752,93436
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| 144 |
143.0,0.32139610646170486,0.7116607420017023,0.15139997005462646,14.4324,178.279,5.612,0.24523902059852312,94094
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| 145 |
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144.0,0.3231559930721119,0.7120749236239796,0.1513846516609192,14.2792,180.193,5.673,0.2495141857753595,94752
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